Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized
medicine and improve healthcare quality. Constructing predictive statistical models …

Methods| From March 3 through 25, 2020, we performed real-time reverse transcriptase–polymerase
chain reaction tests for SARS-CoV-2 and other respiratory pathogens on …

Background and Aims Proton pump inhibitors (PPIs) have been associated with adverse
clinical outcomes amongst clopidogrel users after an acute coronary syndrome. Recent pre-…

Recent advances in supervised machine learning have improved diagnostic accuracy and
prediction of treatment outcomes, in some cases surpassing the performance of clinicians. 1 …

An important goal of the health system is to identify new adverse drug events (ADEs) in the
postapproval period. Data‐mining methods that can transform data into meaningful …

The information explosion in biology makes it difficult for researchers to stay abreast of current
biomedical knowledge and to make sense of the massive amounts of online information. …

The success of foundation models such as ChatGPT and AlphaFold has spurred significant
interest in building similar models for electronic medical records (EMRs) to improve patient …

Biomedical ontologies provide essential domain knowledge to drive data integration, information
retrieval, data annotation, natural-language processing and decision support. BioPortal …

The vision of creating accessible, reliable clinical evidence by accessing the clinical
experience of hundreds of millions of patients across the globe is a reality. The Observational …

The incorporation of machine learning into clinical medicine holds promise for substantially
improving health care delivery. Private companies are rushing to build machine learning into …